In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …

Classification of imbalanced data is a vastly explored issue of the last and present decade
and still keeps the same importance because data are an essential term today and it …

Sentiment analysis is crucial in various systems such as opinion mining and predicting.
Considerable research has been done to analyze sentiment using various machine learning …

k nearest neighbor (kNN) is a simple and widely used classifier; it can achieve comparable
performance with more complex classifiers including decision tree and artificial neural …

Classification of imbalanced data is a well explored issue in the data mining and machine
learning community where one class representation is overwhelmed by other classes. The …

Although a large number of solutions have been proposed to handle imbalanced
classification problems over past decades, many researches pointed out that imbalanced …

The usage of imbalanced databases is a recurrent problem in real-world data such as
medical diagnostic, fraud detection, and pattern recognition. Nevertheless, in class …

Classification of imbalanced datasets is one of the widely explored challenges of the
decade. The imbalance occurs in many real world datasets due to uneven distribution of …

Fuzzy classification is a widely explored research solution of objects in data sciences and
engineering. With the span of time, it got new heights with significant improvements …

Class overlap in imbalanced datasets is the most common challenging situation for
researchers in the fields of deep learning (DL) machine learning (ML), and big data (BD) …